Deep Convolution Neural Networks, or Deep CNN is the most core of the remarkable development in the field of Deep Learning. Though the CNN has been employed to solve character recognition problems in 1990s, it is not until recently that the CNN has become widespread in Machine Learning. For example, in 2012, the CNN significantly outperformed its competitors in an annual software contest, the ImageNet Large Scale Visual Recognition Challenge, and won the contest. After that, the CNN has become a very useful tool in the field of machine learning.
Recently, the CNNs are widely used in a field of an autonomous driving. The CNNs may perform an object detection, a semantic segmentation and a free space detection by processing its own inputted image in the field of the autonomous driving.
Even though such CNNs play an important role in the field of the autonomous driving, there are some partial fields of the autonomous driving where the CNNs have not been studied much. One of them is a field of autonomous parking. The field of the autonomous parking is important because, even though a hazardous accident threatening someone's life may not occur often, many accidents causing financial loss to owners of vehicles may occur very often while parking. If CNNs are used for the field of the autonomous parking, it must be much more economical than a prior art using sensors to detect parking spaces, but applying CNNs to the field of the autonomous parking has not been studied very much yet.